Robot control parameters auto-tuning in trajectory tracking applications

Abstract

Autonomy is increasingly demanded to industrial manipulators. Robots have to be capable to regulate their behavior to different operational conditions, adapting to the specific task to be executed without requiring high time/resource-consuming human intervention. Achieving an automated tuning of the control parameters of a manipulator is still a challenging task, which involves modeling/identification of the robot dynamics. This usually results in an onerous procedure, both in terms of experimental and data-processing time. This paper addresses the problem of automated tuning of the manipulator controller for trajectory tracking, optimizing control parameters based on the specific trajectory to be executed. A Bayesian optimization algorithm is proposed to tune both the low-level controller parameters (i.e., the equivalent link-masses of the feedback linearizator and the feedforward controller) and the high-level controller parameters (i.e., the joint PID gains). The algorithm adapts the control parameters through a data-driven procedure, optimizing a user-defined trajectory-tracking cost. Safety constraints ensuring, e.g., closed-loop stability and bounds on the maximum joint position error are also included. The performance of proposed approach is demonstrated on a torquecontrolled 7-degree-of-freedom FRANKA Emika robot manipulator. The 25 robot control parameters (i.e., 4 link-mass parameters and 21 PID gains) are tuned in less than 130 iterations, and comparable results with respect to the FRANKA Emika embedded position controller are achieved. In addition, the generalization capabilities of the proposed approach are shown exploiting the proper reference trajectory for the tuning of the control parameters.

Sensorless Optimal Switching Impact/Force Controller

“…While the implemented optimal force controller relies on the approach in [41], the here presented experimental results show its implementation for real robotic applications within the proposed OSIF controller (i.e., only simulation results have been provided in [41]).…”

“…q d is obtained integrating qd . The gain matrices K p , K i , K d are tuned on the basis of the methodology in [41].…”

“…To design a robust implementation of the optimal force controller to take into account uncertainties in the environment dynamics modeling (i.e., considering the uncertainties in the estimation of the environment stiffness parameter K e,i ), such uncertainties are included into (41), resulting in the following updated state-expression:…”

“…1) Inner joint position control the diagonal elements of K p,c has been imposed equal to 0.25 1/s; K p = diag([1000, 1500, 1500, 1500, 400, 200, 150]) Nm/rad has been imposed; K d = diag( [32,40,30,45,20,20,10]) Nms/rad has been imposed; K i = diag( [15,15,20,40,40,30,40]) Nm/(s rad) has been imposed; friction compensation as in [49] have been implemented. K p , K d , and K i have been optimized as in [41]. [s] - mass parameters of M have been imposed equal to 10 kg, while the inertia parameters have been imposed equal to 10 kg m 2 ; rotational stiffness parameters of K have been imposed equal to 100 Nm/rad; rotational damping ratio parameters of ζ have been imposed to 1.…”

Precision Interaction Force Control of an Underactuated Hydraulic Stance Leg Exoskeleton Considering the Constraint from the Wearer

“…The proposed control is a model based robust controller, thus, the gain tuning method above is just based on the performance analysis of the closed loop system. In order to further improve the performance, the optimization methods in [40,41] can be considered to obtain optimal controller gains.…”